Embedding
A numeric representation of text, images, or other data used to compare meaning.
What is Embedding?
Embeddings place semantically similar items near one another in a high-dimensional space. They support retrieval, clustering, recommendations, and duplicate detection, but they are not readable summaries of the original content.
In practiceWhat does this look like in a real AI answer?
Two differently worded questions about trial cancellation receive similar text embeddings.
Why it matters for AI visibility
AI answers can shape awareness and buying decisions before a person visits a website. Understanding embedding helps teams connect what a model says with the prompts, sources, and technical signals that influenced the result. The useful next step is to observe the evidence, compare it over time, and act on the specific gap.
How to measure or use it
- Define the scope. Choose the relevant market, prompts, models, locations, and time window.
- Keep the evidence. Save the complete answer, its citations, and the page or claim being evaluated.
- Compare patterns. Look for repeated movement across prompts and models instead of reacting to one response.
- Take a specific action. Improve the owned page, correct a claim, earn third-party inclusion, or create better evidence.